Is there papers that explain how sample weighting is used with each of
those specific classifiers ? In other words, papers explaining how each of
those classifiers is modified to support sample weighting.
2014-06-17 11:31 GMT+02:00 Arnaud Joly :
> Hi,
>
> Without being exhaustive Random forest,
Hi,
Without being exhaustive Random forest, extra trees, bagging, adaboost, naive
bayes and several linear
models support sample weight.
Best regards,
Arnaud
On 17 Jun 2014, at 11:27, Mohamed-Rafik Bouguelia
wrote:
> Hello all,
>
> I've tried to associate weights to instances when trainin
Take a look at the docstring of any classifier and look for
`sample_weight`. If this keyword is provided, you can add sample weights.
Try googling "sklearn sample_weight" or look here
https://github.com/scikit-learn/scikit-learn/search?q=sample_weight for an
overview.
Michael
On Tue, Jun 17, 20
Hello all,
I've tried to associate weights to instances when training an SVM in
sklearn (
http://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html
). Is it possible to use instance weighting with other classifiers from
sklearn (others than the SVM) ? Basically, I know that any
